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import matplotlib.pyplot as plt
import numpy as np
from sklearn.kernel_approximation import RBFSampler
from gudhi.sktda import Landscape, Silhouette, BettiCurve, ComplexPolynomial,\
TopologicalVector, DiagramScaler, BirthPersistenceTransform,\
PersistenceImage, PersistenceWeightedGaussianKernel,\
PersistenceScaleSpaceKernel, SlicedWassersteinDistance,\
SlicedWassersteinKernel, BottleneckDistance, PersistenceFisherKernel
D = np.array([[0.,4.],[1.,2.],[3.,8.],[6.,8.]])
plt.scatter(D[:,0],D[:,1])
plt.plot([0.,10.],[0.,10.])
plt.show()
diags = [D]
LS = Landscape(resolution = 1000)
L = LS.fit_transform(diags)
plt.plot(L[0][:1000])
plt.plot(L[0][1000:2000])
plt.plot(L[0][2000:3000])
plt.show()
def pow(n):
return lambda x: np.power(x[1]-x[0],n)
SH = Silhouette(resolution=1000, weight=pow(2))
sh = SH.fit_transform(diags)
plt.plot(sh[0])
plt.show()
BC = BettiCurve(resolution=1000)
bc = BC.fit_transform(diags)
plt.plot(bc[0])
plt.show()
CP = ComplexPolynomial(threshold=-1, F="T")
cp = CP.fit_transform(diags)
print("Complex polynomial is " + str(cp[0,:]))
TV = TopologicalVector(threshold=-1)
tv = TV.fit_transform(diags)
print("Topological vector is " + str(tv[0,:]))
#diagsT = DiagramPreprocessor(use=True, scalers=[([0,1], BirthPersistenceTransform())]).fit_transform(diags)
#PI = PersistenceImage(bandwidth=1., weight=lambda x: x[1], im_range=[0,10,0,10], resolution=[100,100])
#pi = PI.fit_transform(diagsT)
#plt.imshow(np.flip(np.reshape(pi[0], [100,100]), 0))
#plt.show()
plt.scatter(D[:,0],D[:,1])
D = np.array([[1.,5.],[3.,6.],[2.,7.]])
plt.scatter(D[:,0],D[:,1])
plt.plot([0.,10.],[0.,10.])
plt.show()
diags2 = [D]
def arctan(C,p):
return lambda x: C*np.arctan(np.power(x[1], p))
PWG = PersistenceWeightedGaussianKernel(bandwidth=1., kernel_approx=None, weight=arctan(1.,1.))
X = PWG.fit(diags)
Y = PWG.transform(diags2)
print("PWG kernel is " + str(Y[0][0]))
PWG = PersistenceWeightedGaussianKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])), weight=arctan(1.,1.))
X = PWG.fit(diags)
Y = PWG.transform(diags2)
print("Approximate PWG kernel is " + str(Y[0][0]))
PSS = PersistenceScaleSpaceKernel(bandwidth=1.)
X = PSS.fit(diags)
Y = PSS.transform(diags2)
print("PSS kernel is " + str(Y[0][0]))
PSS = PersistenceScaleSpaceKernel(kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))
X = PSS.fit(diags)
Y = PSS.transform(diags2)
print("Approximate PSS kernel is " + str(Y[0][0]))
sW = SlicedWassersteinDistance(num_directions=100)
X = sW.fit(diags)
Y = sW.transform(diags2)
print("SW distance is " + str(Y[0][0]))
SW = SlicedWassersteinKernel(num_directions=100, bandwidth=1.)
X = SW.fit(diags)
Y = SW.transform(diags2)
print("SW kernel is " + str(Y[0][0]))
W = BottleneckDistance(epsilon=.001)
X = W.fit(diags)
Y = W.transform(diags2)
print("Bottleneck distance is " + str(Y[0][0]))
PF = PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1.)
X = PF.fit(diags)
Y = PF.transform(diags2)
print("PF kernel is " + str(Y[0][0]))
PF = PersistenceFisherKernel(bandwidth_fisher=1., bandwidth=1., kernel_approx=RBFSampler(gamma=1./2, n_components=100000).fit(np.ones([1,2])))
X = PF.fit(diags)
Y = PF.transform(diags2)
print("Approximate PF kernel is " + str(Y[0][0]))
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